{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.tsa import stattools\n",
    "from statsmodels.tsa import seasonal\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Start by setting the current directory and henceforth working relative to it\n",
    "os.chdir('D:/Practical Time Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThis notebook illustrates time series decomposition by the statsmodels package.\\nBoth additive and multiplicative models are demonstrated.\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "This notebook illustrates time series decomposition by the statsmodels package.\n",
    "Both additive and multiplicative models are demonstrated.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nLet us demonstrate the addtive model using Wisconsin Employment\\nJan. 1961 – OCt. 1975 dataset.\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Let us demonstrate the addtive model using Wisconsin Employment\n",
    "Jan. 1961 – OCt. 1975 dataset.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data from into a pandas.DataFrame\n",
    "wisc_emp = pd.read_csv('datasets/wisconsin-employment-time-series.csv')\n",
    "wisc_emp.index = wisc_emp['Month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the DataFrame: (178, 2)\n"
     ]
    }
   ],
   "source": [
    "#Let's find out the shape of the DataFrame\n",
    "print('Shape of the DataFrame:', wisc_emp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Employment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01</th>\n",
       "      <td>1961-01</td>\n",
       "      <td>239.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02</th>\n",
       "      <td>1961-02</td>\n",
       "      <td>236.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03</th>\n",
       "      <td>1961-03</td>\n",
       "      <td>236.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04</th>\n",
       "      <td>1961-04</td>\n",
       "      <td>241.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05</th>\n",
       "      <td>1961-05</td>\n",
       "      <td>243.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06</th>\n",
       "      <td>1961-06</td>\n",
       "      <td>246.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07</th>\n",
       "      <td>1961-07</td>\n",
       "      <td>244.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-08</th>\n",
       "      <td>1961-08</td>\n",
       "      <td>244.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-09</th>\n",
       "      <td>1961-09</td>\n",
       "      <td>244.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-10</th>\n",
       "      <td>1961-10</td>\n",
       "      <td>246.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Month  Employment\n",
       "Month                       \n",
       "1961-01  1961-01       239.6\n",
       "1961-02  1961-02       236.4\n",
       "1961-03  1961-03       236.8\n",
       "1961-04  1961-04       241.5\n",
       "1961-05  1961-05       243.7\n",
       "1961-06  1961-06       246.1\n",
       "1961-07  1961-07       244.1\n",
       "1961-08  1961-08       244.2\n",
       "1961-09  1961-09       244.8\n",
       "1961-10  1961-10       246.6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see first 10 rows of it\n",
    "wisc_emp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values found: 0\n"
     ]
    }
   ],
   "source": [
    "#Check for missing values and remove the row\n",
    "missing = (pd.isnull(wisc_emp['Employment'])) | (pd.isnull(wisc_emp['Month']))\n",
    "print('Number of missing values found:', missing.sum())\n",
    "wisc_emp = wisc_emp.loc[~missing, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Run ADF test on the original time series\n",
    "adf_result = stattools.adfuller(wisc_emp['Employment'], autolag='AIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-val of the ADF test on irregular variations in employment data: 0.981000018954\n"
     ]
    }
   ],
   "source": [
    "print('p-val of the ADF test on irregular variations in employment data:', adf_result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "decompose_model = seasonal.seasonal_decompose(wisc_emp.Employment.tolist(), freq=12,\n",
    "                                              model='additive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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LyFQRyRaRJSJyps/tNU+Pz7ywM0Wkp8+9n0//lSJyQZRhp3m/y7zaYXE4X/ji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\nChEZSRAEQVCIyEiCIAiCQvwPFFVB3i2WnkMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2628520a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the original time series, trend, seasonal and random components\n",
    "fig, axarr = plt.subplots(4, sharex=True)\n",
    "fig.set_size_inches(5.5, 5.5)\n",
    "\n",
    "wisc_emp['Employment'].plot(ax=axarr[0], color='b', linestyle='-')\n",
    "axarr[0].set_title('Monthly Employment')\n",
    "\n",
    "pd.Series(data=decompose_model.trend, index=wisc_emp.index).plot(color='r', linestyle='-', ax=axarr[1])\n",
    "axarr[1].set_title('Trend component in monthly employment')\n",
    "\n",
    "pd.Series(data=decompose_model.seasonal, index=wisc_emp.index).plot(color='g', linestyle='-', ax=axarr[2])\n",
    "axarr[2].set_title('Seasonal component in monthly employment')\n",
    "\n",
    "pd.Series(data=decompose_model.resid, index=wisc_emp.index).plot(color='k', linestyle='-', ax=axarr[3])\n",
    "axarr[3].set_title('Irregular variations in monthly employment')\n",
    "\n",
    "plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=2.0)\n",
    "plt.xticks(rotation=10)\n",
    "\n",
    "plt.savefig('plots/ch2/B07887_02_22.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run ADF test on the irregular variations\n",
    "adf_result = stattools.adfuller(decompose_model.resid[np.where(np.isfinite(decompose_model.resid))[0]],\n",
    "                                autolag='AIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-val of the ADF test on irregular variations in employment data: 0.00656093163464\n"
     ]
    }
   ],
   "source": [
    "print('p-val of the ADF test on irregular variations in employment data:', adf_result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThe additive decompostion has been able to reduce the p-value\\nfrom 0.98 in case of the original time series\\nto 0.066 after decomposing.\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "The additive decompostion has been able to reduce the p-value\n",
    "from 0.98 in case of the original time series\n",
    "to 0.066 after decomposing.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nNow we will attempt decomposition of the original time\\nusing a multiplicative model\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Now we will attempt decomposition of the original time\n",
    "using a multiplicative model\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "decompose_model = seasonal.seasonal_decompose(wisc_emp.Employment.tolist(), freq=12,\n",
    "                                              model='multiplicative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XLrb1eeCB5s8tKipev3WrZQw74ggb/qdbBQPr9toLGja0Brebb7YoiFtusc4T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\nXH0HuevxfOS8BOrr5qa/B04IrHsT+Ge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6AYT22ZKxKoN+e/QL9UYK1hmGjBu3bmRV7ip6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\nlxejB8D26QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fe58b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the original time series, trend, seasonal and random components\n",
    "fig, axarr = plt.subplots(4, sharex=True)\n",
    "fig.set_size_inches(5.5, 5.5)\n",
    "\n",
    "wisc_emp['Employment'].plot(ax=axarr[0], color='b', linestyle='-')\n",
    "axarr[0].set_title('Monthly Employment')\n",
    "\n",
    "axarr[1].plot(decompose_model.trend, color='r', linestyle='-')\n",
    "axarr[1].set_title('Trend component in monthly employment')\n",
    "\n",
    "axarr[2].plot(decompose_model.seasonal, color='g', linestyle='-')\n",
    "axarr[2].set_title('Seasonal component in monthly employment')\n",
    "\n",
    "axarr[3].plot(decompose_model.resid, color='k', linestyle='-')\n",
    "axarr[3].set_title('Irregular variations in monthly employment')\n",
    "\n",
    "plt.savefig('plots/ch2/B07887_02_23.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Run ADF test on the irregular variations\n",
    "adf_result = stattools.adfuller(decompose_model.resid[np.where(np.isfinite(decompose_model.resid))[0]],\n",
    "                                autolag='AIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-val of the ADF test on irregular variations in employment data: 0.00123478372677\n"
     ]
    }
   ],
   "source": [
    "print('p-val of the ADF test on irregular variations in employment data:', adf_result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nVoila! The p-val has further reduced 0.00123.\\nThe null hypothesis about non-stationarity of the irregular variations\\ncan be rejected at even a level of confidence of 99 % (alpha=0.01).\\nThis shows that the original time series has been de-stationarized to\\nthe stationary irregular variations. Besides we have estimates of both trend-cycle\\nand seasonal components.\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Voila! The p-val has further reduced 0.00123.\n",
    "The null hypothesis about non-stationarity of the irregular variations\n",
    "can be rejected at even a level of confidence of 99 % (alpha=0.01).\n",
    "This shows that the original time series has been de-stationarized to\n",
    "the stationary irregular variations. Besides we have estimates of both trend-cycle\n",
    "and seasonal components.\n",
    "\"\"\""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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 },
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